A shape mapping for generalized beta exponential distribution of the first kind in the entropy-parametric space

Abstract Modern methods of tracking and controlling complex systems are associated with the analysis shape statistical models sample arrays. Often values monitored parameters maintained only on a semi-infinite range values. In these cases, it is possible to use generalized beta exponential distribution first kind smooth statistics, that including such as Gompertz-Verhulst distributions special cases. The large set subfamilies rarely due lack preliminary estimation parameters. paper illustrates displayed in parametric space skewness kurtosis does not allow distinguishing features position main distributions. Mapping Generalized Beta Exponential Distribution Entropy-Parametric makes distinguish between comparative many their properties. It convenient entropy coefficient when you comparing skew properties antikurtosis more suitable for weights tails analysing monotonicity. particular, shown contains monotonic non-monotonic leptokurtic property monotonicity well discernible compared distribution.